SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 2976–3000 of 7282 papers

TitleStatusHype
A cross Transformer for image denoisingCode1
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph EmbeddingCode0
Towards More Accurate Diffusion Model Acceleration with A Timestep Aligner—0
Privacy-Preserving Encrypted Low-Dose CT Denoising—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
Price of Stability in Quality-Aware Federated Learning—0
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image DenoisingCode1
Discovery and Expansion of New Domains within Diffusion Models—0
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction—0
Unmasking Bias in Diffusion Model TrainingCode0
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel EstimationCode0
Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging NoiseCode1
Denoising Task Routing for Diffusion ModelsCode1
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion ModelsCode0
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion ProcessesCode1
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Diffusion Models for Wireless Communications—0
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms—0
Echocardiography video synthesis from end diastolic semantic map via diffusion model—0
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion ModelCode1
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT—0
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified